A Multidimensional Concept of Mental Workload: A Systematic Review
DOI:
https://doi.org/10.12928/jehcp.v11i4.24203Abstract
The concept of mental workload is fully used and leads to various theoretical and methodological models. For this purpose, we are conducted in the same way as a systematic review for understanding the concept and a factor that identifies work and work situations that affect personal tasks, or mental workload field. A systematic review was obtained from scientific papers issued from 2010-to 2021. Mental workload is multidimensional, so that a conceptual definition of mental workload should therefore integrally encompass the most elementary dimensions of mental workload. In general, most factors affected mental workloads, including working environments, individual differences, temporal pressure, and task difficulty/compliance complexity. Techniques for assessing subjective workloads are popular in several studies because of their ease of use and sensitivity to workload fluctuations. The NASATLX scale is the most common subjective technique and is used in a wide range of fields. Subjective and objective measurements cannot even measure all kinds of factors that affect mental distress. The main difficulty facing researchers is establishing standardized measurements of mental workload and its normal range so that effective comparisons can be made between groups of subjects. These results can provide measurement development recommendations using three approaches: subjective, objective, and behavioral.
Keywords: mental workload; measurement; workload factors
References
Alexandre, K., Rauffet, P., Chauvin, C., dan Coppin, G. (2016). A dynamic closed-looped and multidimensional model for Mental Workload evaluation (Vol. 49). https://doi.org/10.1016/j.ifacol.2016.10.621
Antonenko, P., Paas, F., Grabner, R., dan van Gog, T. (2010). Using Electroencephalography to Measure Cognitive Load. Educational Psychology Review, 22(4), 425-438. https://doi.org/10.1007/s10648-010-9130-y
Ayaz, H., Shewokis, P. A., Bunce, S., Izzetoglu, K., Willems, B., dan Onaral, B. (2012). Optical brain monitoring for operator training and mental workload assessment. NeuroImage, 59(1), 36-47. https://doi.org/https://doi.org/10.1016/j.neuroimage.2011.06.023
Backs, R. W., Lenneman, J. K., Wetzel, J. M., dan Green, P. (2003). Cardiac Measures of Driver Workload during Simulated Driving with and without Visual Occlusion. Human Factors, 45(4), 525-538. https://doi.org/10.1518/hfes.45.4.525.27089
Bommer, S. C., dan Fendley, M. (2018). A theoretical framework for evaluating mental workload resources in human systems design for manufacturing operations. International Journal of Industrial Ergonomics, 63, 7-17. https://doi.org/https://doi.org/10.1016/j.ergon.2016.10.007
Boucsein, W. (2012). Electrodermal activity. Springer Science & Business Media.
Braarud, P. Ø. (2020). An efficient screening technique for acceptable mental workload based on the NASA Task Load Index—development and application to control room validation. International Journal of Industrial Ergonomics, 76, 102904. https://doi.org/https://doi.org/10.1016/j.ergon.2019.102904
Byrne, A. (2011). Measurement of mental workload in clinical medicine: a review study. Anesthesiology and pain medicine, 1(2), 90-94. https://doi.org/https://doi.org/10.5812/kowsar.22287523.2045
Byrne, A., Tweed, N., dan Halligan, C. (2014). A pilot study of the mental workload of objective structured clinical examination examiners [https://doi.org/10.1111/medu.12387]. Medical Education, 48(3), 262-267. https://doi.org/https://doi.org/10.1111/medu.12387
Cain, B. (2007). RTO-TR-HFM-121-Part-II-Human Factors Considerations in the Design, Use, and Evaluation of AMVE-Technology. A Review of the Mental Workload Literature. http://ftp.rta.nato.int/public//PubFullText/RTO/TR/RTO-TR-HFM-121-PART-II///TR-HFM-121-Part-II-04.pdf
Di Stasi, L. L., Antolí, A., dan Cañas, J. J. (2011). Main sequence: An index for detecting mental workload variation in complex tasks. Appl Ergon, 42(6), 807-813. https://doi.org/https://doi.org/10.1016/j.apergo.2011.01.003
DiDomenico, A., dan Nussbaum, M. A. (2011). Effects of different physical workload parameters on mental workload and performance. International Journal of Industrial Ergonomics, 41(3), 255-260. https://doi.org/https://doi.org/10.1016/j.ergon.2011.01.008
Ding, Y., Cao, Y., Duffy, V. G., Wang, Y., dan Zhang, X. (2020). Measurement and identification of mental workload during simulated computer tasks with multimodal methods and machine learning. Ergonomics, 63(7), 896-908. https://doi.org/10.1080/00140139.2020.1759699
Fallahi, M., Motamedzade, M., Heidarimoghadam, R., Soltanian, A. R., dan Miyake, S. (2016). Effects of mental workload on physiological and subjective responses during traffic density monitoring: A field study. Appl Ergon, 52, 95-103. https://doi.org/https://doi.org/10.1016/j.apergo.2015.07.009
Galy, E., Cariou, M., dan Mélan, C. (2012). What is the relationship between mental workload factors and cognitive load types? International Journal of Psychophysiology, 83(3), 269-275. https://doi.org/https://doi.org/10.1016/j.ijpsycho.2011.09.023
Galy, E., Paxion, J., dan Berthelon, C. (2018). Measuring mental workload with the NASA-TLX needs to examine each dimension rather than relying on the global score: an example with driving. Ergonomics, 61(4), 517-527. https://doi.org/10.1080/00140139.2017.1369583
Grier, R. A. (2015). How High is High? A Meta-Analysis of NASA-TLX Global Workload Scores. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 59(1), 1727-1731. https://doi.org/10.1177/1541931215591373
Gurses, A. P., dan Carayon, P. (2009). Exploring performance obstacles of intensive care nurses. Appl Ergon, 40(3), 509-518. https://doi.org/10.1016/j.apergo.2008.09.003
Hertzum, M., dan Holmegaard, K. D. (2013). Perceived Time as a Measure of Mental Workload: Effects of Time Constraints and Task Success. International Journal of Human–Computer Interaction, 29(1), 26-39. https://doi.org/10.1080/10447318.2012.676538
Jeffri, N. F. S., dan Awang Rambli, D. R. (2021). A review of augmented reality systems and their effects on mental workload and task performance. Heliyon, 7(3), e06277-e06277. https://doi.org/10.1016/j.heliyon.2021.e06277
Longo, L. (2015). A defeasible reasoning framework for human mental workload representation and assessment. Behaviour & Information Technology, 34(8), 758-786. https://doi.org/10.1080/0144929X.2015.1015166
Mandrick, K., Chua, Z., Causse, M., Perrey, S., dan Dehais, F. (2016). Why a Comprehensive Understanding of Mental Workload through the Measurement of Neurovascular Coupling Is a Key Issue for
Neuroergonomics? [Opinion]. Frontiers in Human Neuroscience, 10. https://doi.org/10.3389/fnhum.2016.00250
Marchand, C., De Graaf, J. B., dan Jarrassé, N. (2021). Measuring mental workload in assistive wearable devices: a review. Journal of NeuroEngineering and Rehabilitation, 18(1), 160. https://doi.org/10.1186/s12984-021-00953-w
Marinescu, A., Sharples, S., Ritchie, A. C., López, T. S., McDowell, M., dan Morvan, H. (2016). Exploring the Relationship between Mental Workload, Variation in Performance and Physiological Parameters. IFAC-PapersOnLine, 49(19), 591-596. https://doi.org/https://doi.org/10.1016/j.ifacol.2016.10.618
Marinescu, A. C., Sharples, S., Ritchie, A. C., Sánchez López, T., McDowell, M., dan Morvan, H. P. (2018). Physiological Parameter Response to Variation of Mental Workload. Hum Factors, 60(1), 31-56. https://doi.org/10.1177/0018720817733101
Matthews, G., Reinerman-Jones, L. E., Barber, D. J., dan Abich, J. (2014). The Psychometrics of Mental Workload: Multiple Measures Are Sensitive but Divergent. Human Factors, 57(1), 125-143. https://doi.org/10.1177/0018720814539505
Mehta, R., dan Parasuraman, R. (2013). Neuroergonomics: a review of applications to physical and cognitive work [Review]. Frontiers in Human Neuroscience, 7. https://www.frontiersin.org/article/10.3389/fnhum.2013.00889
Mohammadi, M., Mazloumi, A., Kazemi, Z., dan Zeraati, H. (2016). Evaluation of Mental Workload among ICU Ward's Nurses. Health promotion perspectives, 5(4), 280-287. https://doi.org/10.15171/hpp.2015.033
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., dan The, P. G. (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLOS Medicine, 6(7), e1000097. https://doi.org/10.1371/journal.pmed.1000097
Ozkan, A., Ozdevecioglu, M., Kaya, Y., dan Koç, F. Ö. (2015). Effects of mental workloads on depression–anger symptoms and interpersonal sensitivities of accounting professionals. Revista de Contabilidad, 18(2), 194-199. https://doi.org/https://doi.org/10.1016/j.rcsar.2014.06.005
Rusnock, C. F., dan Borghetti, B. J. (2018). Workload profiles: A continuous measure of mental workload. International Journal of Industrial Ergonomics, 63, 49-64. https://doi.org/https://doi.org/10.1016/j.ergon.2016.09.003
Shakouri, M., Ikuma, L. H., Aghazadeh, F., dan Nahmens, I. (2018). Analysis of the sensitivity of heart rate variability and subjective workload measures in a driving simulator: The case of highway work zones. International Journal of Industrial Ergonomics, 66, 136-145. https://doi.org/https://doi.org/10.1016/j.ergon.2018.02.015
Shamseer, L., Moher, D., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., . . . Stewart, L. A. (2015). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. BMJ : British Medical Journal, 349, g7647. https://doi.org/10.1136/bmj.g7647
Tufanaru, C., Munn, Z., Aromataris, E., Campbell, J., dan Hopp, L. (2019). Systematic review of effectiveness. In JBI Manual for Evidence Synthesis. https://wiki.jbi.global/display/MANUAL/Chapter+3%3A+Systematic+reviews+of+effectiveness
Van Acker, B. B., Parmentier, D. D., Vlerick, P., dan Saldien, J. (2018). Understanding mental workload: from a clarifying concept analysis toward an implementable framework. Cognition, Technology & Work, 20(3), 351-365. https://doi.org/10.1007/s10111-018-0481-3
Van der Kleij, R., Hueting, T., dan Schraagen, J. M. (2018). Change detection support for supervisory controllers of highly automated systems: Effects on performance, mental workload, and recovery of situation awareness following interruptions. International Journal of Industrial Ergonomics, 66, 75-84. https://doi.org/https://doi.org/10.1016/j.ergon.2018.02.010
Wickens, C. D., Helton, W. S., Hollands, J. G., dan Banbury, S. (2021). Engineering Psychology and Human Performance (5th ed.). Routledge. https://doi.org/ https://doi.org/10.4324/9781003177616
Wihardja, H., Hariyati, R. T. S., dan Gayatri, D. (2019). Analysis of factors related to the mental workload of nurses during interaction through nursing care in the intensive care unit [10.1016/j.enfcli.2019.06.002]. Enfermería Clínica, 29, 262-269. https://doi.org/10.1016/j.enfcli.2019.06.002
Young, M. S., Brookhuis, K. A., Wickens, C. D., dan Hancock, P. A. (2015). State of science: mental workload in ergonomics. Ergonomics, 58(1), 1-17. https://doi.org/10.1080/00140139.2014.956151
Young, M. S., dan Stanton, N. A. (2004). Mental workload. In Handbook of human factors and ergonomics methods (pp. 416-426). CRC Press.
